(2009), Recasens and Hovy (2010), Chen and Ng (2012a)).

no attempts have been made to address these questions in the context of event coreference.


Keyphrases are useful for a variety of tasks such as summarization (Zha, 2002), information retrieval (Jones and Staveley, 1999) and document clustering (Han et al., 2007).

many documents do not come with manually assigned keyphrases.


(Litvak et al., 2011) is one of them, where degree centrality is used to select keyphrases.

they evaluate their method indirectly through a summarization task, and to our knowledge there are no published experiments using other centrality measures for keyphrase extraction.


(2011) in which TextRank and degree centrality are compared.

both works were evaluated against a summarization dataset by checking whether extracted keyphrases appear in reference summaries.


A prerequisite of the above methods is that the unknown words must have paraphrases (or full-forms).

many types of unknown words do not have paraphrases (full-forms) naturally.


(2011) developed a sublexical translation method that translates an unknown word by combining the translations of its sublexicals.

to deal with the reordering problem, the model combines the translations of sublexicals by considering both straight and inverse directions and uses a language model to select the better one.


In recent years, web services are generating more and more short texts including micro-blogs, customer reviews, chat messages and so on.

a user is often only interested in very small part of these data.


In English it is easy to determine which words are within a specific window since each word split by spaces usually corresponds to one POS tag.

unlike English, Korean is an agglutinative language and most words consist of more than one morpheme, each with their own part of speech.


Assuming that tweets are retrieved by a query, such as "apple", the task is to classify whether each retrieved tweet is relevant to the target organization ("Apple Inc.") or not.

constructing such a classifier is a challenging task, as tweets are short and informal.


Kim and Hovy (2006) proposed a method to identify a reason for the evaluation in an opinion, such as "the service was terrible because the staff was rude" and "in a good location close to the station".

their purpose is to identify grounds that justify the evaluation, which are different from evaluative conditions.


The two words have different roots and are therefore genetically unrelated.

for language learners the similarity is more evident than for example the English-Italian genetic cognate father-padre.


For these reasons, both types need to be considered when constructing teaching materials.

existing lists of cognates are usually limited in size and only available for very few language pairs.


As we have seen in the experiments in Section 4.1, COP is able to learn a production rule from only few training instances.

the test dataset contains a variety of cognates following many different production processes.


Thus, COP is able to learn stable patterns from relatively few training instances.

even a list of 1,000 cognates is a hard constraint for some language pairs.


It is clearly that a different set of features could be used for learning the NB classifier at the first classification stage in our system.

as mentioned in section 3.4, it is sufficient to have a good NB classifier learned from an unique DiffPosNeg feature.


(2012) developed a universal Part-of-Speech (POS) tag-set for twenty five different languages.

at phrasal level, disagreements between the languages remain undefined.


Most keyword tools can easily identify keywords the old house (謝宅) and the historical city (台南).

article readers might also be interested in less frequent words like life style (生活) and traditional market (市場), and singleoccurrence like rental fees (費用), which are also mentioned in most reader feedback.


This problem can be traditionally formulated as a text classification task and solved by annotating the data and building a supervised learning system.

rare classes might render annotation even more difficult and expensive.


Most basic features consider that word pairs are much less likely to have a dependency relation when there are punctuation between them.

based on the fact that dependencies with longer distance always show worse parsing performance (McDonald and Nivre, 2007), distance is another important factor that reflects the difficulty of judging whether two words have a dependency relation.


This difficulty interferes with efficient language resource preparation and reduces domain portability.

the accuracy of P-A structure analysis increases in accordance with the data size.


As we can see from Table 3, we still obtain significantly inferior results compared to the original translation if we replace all the Google translations by the most similar examples, which is reflected by an absolute 8.55 point drop on the test set in BLEU score.

our repairing method, which can repair original translation result automatically in word-level, leads to an increase of 0.64 absolute BLEU point on the test set.


The work of (Riesa and Marcu, 2012) detected parallel fragments using the hierarchical alignment model.

this approach obtains fragments from parallel sentence pairs, which limits its application in comparable corpora.


Parallel data in the real world is increasing continually.

we cannot always get the translation performance improved by simply enlarging our training data.


In this paper, we explore the possibility of using dependency structure for anaphora resolution in Hindi.

we do not intend either to propose dependency as an alternative to phrase structure or to compare the usability of the two frameworks.


For example, the future prefix ,سـ( s, "I will") is transformed in TD to ( ,با bA$, "I will").

the interrogation prefix "أ" is transformed to a suffix ,شي( $y, "what").


Thus, on the P&N model, the average conditional entropy per feature given the class (how surprising the feature is when we know the answer) increases by 8.8% when the oracle is unavailable.

there is almost no difference between the conditional entropy of the POS model with oracle features and without, indicating that the errors made by the tagger are not confusing in the disambiguation task.


Predicted parse features also contribute to feature sparsity, because of the greater variability of automatic parses.

they are more expressive than part of speech, and in the example below, where only Lin correctly identifies 'and' as a discourse connective, part of speech simply does not contain enough information.


These can be better predicted if dependency relations are given as input.

the standard natural language analysis pipeline forbids using parse information during morphological analysis.


Given that fact, (Burchardt et al., 2009) pointed out that using shallow semantic representations based on predicate-argument structures and frame knowledge is an intuitive and straightforward approach to textual inference tasks.

to previous work which integrate predicate-argument structures as features in machine learning-based systems (Harabagiu et al., 2006;De Marneffe et al., 2008), this paper combines shallow semantic representations derived from semantic role labeling with binary relations extracted from sentences for the CD task.


The obtained results indicate that for semantic trees with at least 500 nodes, the performance of our method increases consistently.

the F-Score reaches the top and becomes stable for semantic tree sizes between 2,000 and 3,000 nodes.


incremental speech translation has been addressed in simultaneous translation of lectures and speeches (Hamon et al., 2009;Fügen et al., 2007).


Processing pipeline and service composition are two approaches for sharing and combining language resources.


(2012) proposed a statistical model that was capable of learning how to pre-order word sequences from human annotated or automatically generated alignment data.

this method has very large computational complexity to model long distance reordering.


In the example, since there is no subject and object, the verbal head "示す show(s)" is moved to immediately before its second dependent.

v has been incorrectly placed in the rightmost in Komachi et al.


There is still a minor verb agreement error which the verb "represent" is translated as "represents".

the most of errors are given via parsing process.


Comparative experiments show that our approach could efficiently reduce feature dimensionality and enhance the final F 1 value.

for automatic grammatical error correction, there is still a long way to go.


(2010), with per-outer-iteration example caching (LCLR); we use a PA large-margin classifier instead of an SVM.

we found that this algorithm severely overfits to our task.


In general the larger the size of the monolingual corpus, the better and more detailed, we can extract the context, or context vectors for each relevant word (Curran and Moens, 2002).

in a specific domain, the given monolingual corpus might be limited to a small size which leads to sparse context vectors.


Given a text snippet in which the ambiguous word occurs, their methods select the appropriate sense by finding an appropriate translation.

our method does not use a text snippet to disambiguate the meaning of the query word.


We could now combine all context vectors additively, similar to monolingual disambiguation like in (Schütze, 1998).

this would ignore that actually some dimensions are difficult to compare across the two languages.


A variety of methods have been proposed for attribute-value extraction from semistructured text with consistent templates (strict semi-text).

when the templates in semi-structured text are inconsistent (weak semi-text), these methods will work poorly.


Semistructured text (strict semi-text) often has distinctive HTML tags and consistent templates like HTML tables (eg: Wikipedia infoboxes).

a lot of user-generated semi-structured text with weak structures exist, where their templates generating records are inconsistent and the HTML tags in these templates are less distinctive.


Traditionally, researchers have tried to estimate the sentiment polarity from only the textual content of the review (Pang and Lee, 2004;.

since reviews are written by a user to express his/her emotion toward a particular product, taking the users and products into consideration would play an important role in solving this task.


They then classify a given review by referring to ratings given for the same product by other users who are similar to the user in question.

such user networks are not always available in the real world.


In the example, we want the annotations, (0, 8, Protein) 2 from A and (0,10, Signaling_molecule) from B, to be transferable to C, or to each other.

the variation of text poses a challenge: we need to compute the mapping between variations of text.


Hewavitharana and Vogel (2011) propose a method that calculates both the inside and outside probabilities for fragments in a comparable sentence pair, and show that the context of the sentence helps fragment extraction.

the proposed method only can be efficient in a controlled manner that supposes the source fragment was known, and search for the target fragment.


So far, the induced transduction grammar have only been used to derive Viterbi-style word alignments to feed into existing translation system, and there has been no evaluation of the grammars actually learned.

we directly evaluate the grammars that we induce.


The extended set reaches the best accuracy scores (76.8%) for the system trained on SYSTRAN translations with a 8.8pt absolute improvement over the baseline set.

statistical significance testing show that none of the improvements over the baseline are statistically significant.


Perhaps the baseline feature set is already diverse enough (surface, LM, word alignment, etc.).

an error analysis shows that including the UGC features does bring useful information, especially when the source segments contain URLs, as shown in Table 5.


Given a parallel corpus between the source and target language, combining a direct model based on this parallel corpus with a pivot model could lead to better coverage and overall translation quality.

the combination approach needs to be optimized in order to maximize the information gain.


That's why NEs schemes do not show any improvement using static integration.

s_NN shows some inconsistent improvements depending on the data sparsity.


this at the cost of relatively low precision (around 70% even when using the information about the correct word sense).

we propose to use a closed word class -i.e.


Conditional Random Fields were found to provide the best overall results in cue detection.

the relative advantage of sequence taggers  In our classification setup, each token is a separate instance, described by a set of features collected from a window of size 2 around the token.


Chinese Input Method Engine (IME) plays an important role in Chinese language processing.

it has been subjected to lacking a proper evaluation metric for a long time.


The frequencies of lastly mined entries can be simply accumulated to the existing entries.

we allow the users to upload their input logs to the cloud and execute the mining process to extract single user oriented personally entries.


From the definition of coherent interaction type, one would expect a higher percentage of links than strong links.

we found almost equal percentage of strong links and links.


The higher the entropy, the more uncertain the oracle is about the prediction.

there is no readily available provision indicating the magnitude of confusion the oracle encounters during prediction.


(2012) built supervised POS taggers for 22 European languages using the TNT tagger (Brants, 2000), with an average accuracy of 95.2%.

creating annotated linguistic resources is expensive and time-consuming.


English is commonly used, because parallel data which has English on one side is often most readily available.

the appropriate source language might depend on the target language.


We have shown that our predictive model can select a source language -based on only monolingual features of the source and target languages -that improves tagger accuracy compared to choosing the single, best-overall source language.

if parallel data is available, our predictive model is able to leverage this to select a more appropriate source language and obtain further improvements in accuracy.


This makes it central to the semantics-syntax-intonation interface (Lambrecht, 1994;Hajičová et al., 1998;Steedman, 2000;Mel'čuk, 2001;Erteschik-Shir, 2007) and therefore also to NLP.

despite its prominence, IS has been largely ignored so far in the context of the reference treebanks for data-driven NLP: Penn Treebank (Marcus et al., 1993) and its semantic counterpart PropBank (Palmer et al., 2005) for English, Tiger (Thielen et al., 1999) for German, Ancora (Taulé et al., 2008) for Spanish, etc.


This is not to say that no proposals have been made for the annotation of IS in general; see, e.g., (Calhoun et al., 2005) for English, (Dipper et al., 2004) for German, (Paggio, 2006) for Danish, etc.

in the light of the above mentioned interface, it is crucial to have the same corpus annotated with semantic, syntactic and IS structures.


A direct comparison with other works on automatic annotation with IS, as e.g., (Postolache et al., 2005) with TFA, is not possible since the data sets and the annotation schemata are different; see, e.g., (Hajičová, 2007) for a precise outline of the criteria for the annotation of TFA in the Prague school and a juxtaposition of TFA and the CommStr.

it is instructive to observe that the AS we achieve with the transition parser is about the same as Postolache et al.


At the end of this procedure, a segmentation result that fully matches the demand of the user is returned.

in some complicated cases where segmentation ambiguities exist, the Kalman filter will not converge and keep swapping in two or more states.


It matches the problem setting in this paper because it can train a new model by altering the original model to suit the additional data.

it usually loses information about old samples (in our case, the original data).


(Final update formulae are provided in (Wang et al., 2012)).

there are some problems in implementing Equation (3) directly.


Focusing on case (c), in which training uses both the original and the additional datasets, the advantages of cases (a) and (b) are secured.

although we applied domain adaptation, the improvements from cases (a) and (b) were little.


This result shows that model adaptation worked effectively.

focusing on the accuracies of Test set 1, Online learning exhibited a smaller degradation from the original model (a) than Transfer.


The maximum entropy method optimizes parameters based on the maximum a posteriori (MAP), and it is sensitive to probability distribution.

sCW-I used in Online is based on margin criteria, and ignores data outside the margin.


In the normal case (e), the accuracies of Test set 2 improved with both the Transfer and the Online cases along with dataset size.

the accuracy of Test set 1 with Transfer degraded faster than Online, as described in Section 3.2.


Here, hyperparameter C OR was set when the original model   In the normal case, while the changes to the existing parameters were suppressed (small C AD ), the accuracy of Test set 2 decreased.

it was higher than that of the original model (61.49% → 67.26%), and the accuracy on Test set 1 was almost constant (70.90% → 70.33%).


For instance, French lacks a case system (Dryer and Haspelmath, 2011), and makes instead use of prepositions.

polish and Czech most extensively use (inflectional) affixes (Kulikov et al., 2006).


These methods will not detect similarity of sentences that use different words to convey the same meaning.

they achieve improved results by examining word pairs instead of single words (Okazaki et al., 2003).


They found that the use of deep syntactic features reduces pattern ambiguity and dramatically increases overall relation extraction f -measure by 65%.

they do not model selectional restrictions in their pattern gen-eration step.


The tagging performance seems low for practical purposes.

this reflects the lexicon quality (i.e., the bootstrapping performance) only partially.


We already explained that running original Basilisk on the whole EPO corpus is not feasible due to its size and the length of the sentences.

for a comparison of Basilisk-G and Basilisk-C with the original Basilisk, we parsed sentences up to a size of 100 tokens from 25,000 sample patents.


In predominant classes such as SUBSTANCE, shorter coordinations do not harm precision.

for classes like DISEASE, precision decreases when shorter coordinations are used, as illustrated in Table 3.


Previous work in TempEval-2 (Verhagen et al., 2010) and our work (Hovy et al., 2012) have shown that accurate relation classifiers can be modeled with supervised approaches, provided that the expressions are limited to be in the same sentence.

there is almost no previous work on inter-sentence TERE (ISTERE), for three main reasons: • Across a document, the number of time-event pairs to consider is large, as they are quadratic in the number of time and event expressions.


In particular, product attributes and their values (PAVs) are crucial for many applications such as faceted navigation and recommendation.

since structured information is not always provided by the merchants, it is important to build technologies to create this structured information (such as PAVs) from unstructured data (such as a product description).


Instead, the grape variety, production area, and vintage of the wine would be of greater interest.

(2) Freebase contains PAVs for limited types of products such as digital cameras 2 .


(2) On the other hand, Freebase contains PAVs for limited types of products such as digital cameras 2 .

since Freebase is currently only available in English, we cannot use Freebase in a distant supervision method for other languages.


The goal of these approaches is to extract information from documents semi-structured by any mark up language such as HTML.

our method aims at extracting (product) information from full texts although the method leverages semi-structured documents to induce KBs.


6 For instance, for the Abortion domain, the phrase I support abortion indicates the author's support for abortion.

i think abortion should be banned is indicative of the author's stance against abortion.


For ABO and GAY, the improvement that we obtain out of the noisy data decreases as we increase the number of (cleanly labeled) debate posts.

for OBA and MAR, we do not see such diminishing returns.


For example, the Halo project (Angele et al., 2003) targeted Chemical tests, while IBM's Deep QA (Ferrucci, 2012) employed factoid-style quizzes.

their benchmark data sets are not open, and therefore collaborative research based on shared standard data cannot be pursued.


This makes sense, because in a linguistically agnostic situation, all the links have the same weight, and hence the weight assigned to C i j will be the same regardless of which |C i j | − 1 links in C i j are chosen.

the same is no longer true in a linguistically aware setting: since the links may not necessarily have the same weight, the weight assigned to C i j depends on which |C i j | − 1 links are chosen.


In other words, precision is computed by reversing the roles of the key partition K(d) and the system partition S(d) used to compute recall for document d. If we wanted precision and recall to also play a symmetric role in the linguistically aware versions of these scoring metrics, it would be natural to define w L s in the same way as w L k , where E is the set of edges appearing in the maximum spanning tree defined over the mentions in S j .

there is a reason why it is undesirable for us to define w L s in this manner.


Research shows that topical discussions around events tend to evolve socially on microblogs.

sources like Twitter have no explicit discussion thread which will link semantically similar posts.


Those formulas, despite of their simplicity, performed well and were widely used by editors to grade reading material for young readers.

content producers might be tempted to adapt their manuscripts by tweaking the text features present in readability formulas, without gaining (or even degrading) real readability (Davison and Kantor, 1982).


Those features alone were found not to be as discriminant as the lexical ones, but performed well in combination with them.

the effects were not additive, which suggests that variables correlated with each other to a certain extent.


(Blair-Goldensohn and McKeown, 2006;Bosma, 2004)) to improve coherence and better simulate human writing.

most of these work have been developed for formal, well-written and factual documents.


Indeed, Table 1 shows that illustration, contingency, and comparison relations occur quite frequently irrespective of the textual genre.

in contrast to the TAC dataset, attributive, topic-opinion, and attribution relations occur very rarely in DUC 2007.


For example, if TAC-Best does not consider illustration relations, then the R-2 score decreases from 0.138 to 0.112, 0.102 and 0.113, respectively.

the relations of topic-opinion, attribution, and attributive do not consistently lead to a statistically significant improvement on ROUGE scores.


This procedure has been observed in human summarization (Jing and McKeown, 2000) and has been shown to be a valuable component of automated summarization systems (Barzilay and McKeown, 2005).

research in sentence fusion has long been hampered by the absence of datasets for the task, and the difficulty of generating one has cast doubt on the viability of automated fusion (Daumé III and Marcu, 2004).


The precision and recall measures for each experiment are described in Tables 1-3  Following the native speakers' intuitions from the experiment described in Section 3.2, we can assume that the discriminative power of errors should not be surprising; learners of Czech of a NIE language background are likely to make more errors than the learners of the IE group due to the differences between L1 and L2.

we need to perform a more detailed error analysis to conform or disagree with these intuitions.


Thus, greater differences between L1 and L2 grammatical structures might trigger a higher amount of errors within the NIE group.

a more detailed analysis of error distribution within each group and probably a larger data set are needed to investigate this claim.


1 Neural machine translation (NMT) offers an elegant end-to-end architecture, while at the same time improving translation quality.

little is known about the inner workings of these models and their interpretability is limited.


's model also employs dependency parsing but their model separately predicts the target translation sequence and parsing action sequence which maps to translation.

our proposed model's decoder directly predicts the linearized dependency tree itself in a single neural network in Depth-first preorder order so that the next-word token is generated based on syntactic relations and tree construction itself.


The neural system for their experimental analysis is not an attentional model and they argue that attention does not have any impact for learning syntactic information.

performing the same analysis for morphological information, Belinkov et al.


Considering this difference and the observations in Section 5.1, a natural follow-up would be to focus on getting the attention of verbs to be closer to alignments.

figure 3b shows that the average word prediction loss for verbs is actually smaller compared to the loss for nouns.


The low correlation for verbs confirms that attention to other parts of source sentence rather than the aligned word is necessary for translating verbs and that attention does not necessarily have to follow alignments.

the higher correla-  tion for nouns means that consistency of attention with alignments is more desirable.


Their approach uses word embeddings learned from a large-scale native corpus to address the data sparseness problem of learner corpora.

most of the word embeddings, including the one used by , model only the context of the words from a raw corpus written by native speakers, and do not consider specific grammatical errors of language learners.


EWE learns word embeddings using the same model as C&W embeddings.

rather than creating negative samples randomly, we created them by replacing the target word w t with words w c that learners tend to easily confuse with the target word w t .


Our experiments on FCE+EWE-L8 and FCE+E&GWE-L8 were conducted by combining error patterns from all of Lang-8 corpus and the training part of FCE-public corpus to train word embeddings.

since the number of error patterns of Lang-8 is larger than that of FCE-public, we normalized each frequency so that the ratio was 1:1.


They use parsers coarsely trained on ex-isting data with FA for completion via constrained decoding.

our experiments show that this leads to dramatic decrease in parsing accuracy.


This is a consequence of having to predict the probability distribution over an entire vocabulary V , which is generally very large in the real world.

the WON predicts the probability distribution over entire sentences, whose length N is usually less than 50 |V |.


Their method heavily relies on pre-trained dependency parsers to produce words' relations for each sentence in training corpora, thus encountering error propagation problems.

our method only requires raw corpora, and our aim is to produce word embeddings that improve syntax-related tasks, such as parsing, without using any human annotations.


(2015) evaluated the PtrNet on geometric sorting tasks (e.g., Travelling Salesman Problem) where each input w i forms a continuous vector that represents the cartesian coordinate of the point (e.g., a city).

in the word ordering task, Equation 10 suffers from the data sparseness problem, as each input w i forms a high-dimensional discrete symbol.


The bi-directional bilingual pivoting of PPDB (Ganitkevitch et al., 2013) constrains paraphrase acquisition to be strictly symmetric.

although it is extremely effective for extracting synonymous expressions, it tends to give high scores to frequent but irrelevant phrases, since bilingual pivoting itself contains noisy phrase pairs because of word alignment errors.


Language Modeling Language models, from ngram models to continuous space language models (Mikolov et al., 2013;Pennington et al., 2014), provide probability distributions over sequences of words and have shown their usefulness in many natural language processing tasks.

to our knowledge, they have not yet been used to model semantic frames.


Then the two-layered SVM classifier re-predicted whether there was an inference relation for the lexical pair w 1 → w 3 .

none of these models takes into account transitivity in the observed layer or transitivity between two layers.


Therefore, their transitivity framework may involve the noise from the first prediction.

in our PSL models, all possible feature-layered transitivities between pairs are explored.


This is likely because it is easier to learn the parameters of the image prediction model that has fewer parameters (8.192 million for VGG-19 vs. 4.096 million for Inception-V3 and ResNet-50).

it is not clear why there is such a pronounced difference between the Inception-V3 and ResNet-50 models 4 .


(2017a) found that word representations learned from the encoder are rich in morphological information, while representations learned from the decoder are significantly poorer.

the paper does not present a convincing explanation for this finding.


We did not apply byte-pair encoding (BPE) (Sennrich et al., 2016b), which has recently become a common part of the NMT pipeline, because both our analysis and the annotation tools are word level.

2 experimenting with BPE and other representations such as character-based models (Kim et al., 2015) would be interesting.


DEC t i probably encodes morphological information about both the current word (t i ) and the next word (t i+1 ).

we leave this exploration for future work, and work with the assumption that DEC t i encodes information about word t i .


For example, it is possible to incorporate neural features into traditional SMT models to disambiguate hypotheses (Neubig et al., 2015;Stahlberg et al., 2016).

the search space of traditional SMT is usually limited by translation rule tables, reducing the ability of these models to generate hypotheses on the same level of fluency as NMT, even after reranking.


In T 1 , "hypophysectomized (hypop hy sec to mized)" is incorrectly translated into "低(low) 酪(cheese) 蛋白(protein) 切除(remove)".

from Table 9, we can see that the forced decoding algorithm learns it as unlikely translation (hy→低(low)), over-translation (null→酪(cheese), null→蛋 白(protein)) and under-translation (hypop→null, sec→null), because there is no translation rule between "hypop" "sec" and "酪(cheese)" "蛋白(protein)".


One of the strengths of neural networks is the ability to learn features automatically.

this strength has not been well exploited in their works.


(2016);  utilized word embeddings to boost performance of word-based CWS models.

for character-based CWS models, word information is not easy to be integrated.


When we increase the depth from 1 to 5, the performance is improved significantly.

when we increase depth from 5 to 7, even to 11 and 15, the performance is almost unchanged.


The feature experiments indicate that concatenated ngrams contribute substantially.

both radicals and graphical features as sub-character level information are less effective.


Distributional models, which describe the meaning of a word in terms of its observed contexts (Turney and Pantel, 2010), have been suggested as a model for how humans learn word meanings (Landauer and Dumais, 1997).

distributional models typically need hundreds of instances of a word to derive a highquality representation for it, while humans can often infer a passable meaning approximation from one sentence only (as in the above example).


(2016) were the first to explore fast mapping for text-based word learning, using an extension to word2vec with both textual and visual features.

they model the unknown word simply by averaging the vectors of known words in the sentence, and do not explore what types of knowl-edge enable fast mapping.


Word representations based on the distributional hypothesis of Harris (1954) have become a dominant approach including word2vec (Mikolov et al., 2013) and GloVe (Pennington et al., 2014), which show remarkable performances in a wide spectrum of natural language processing.

a question arises about a relationship between a true word meaning and its distributed representation.


For instance, since concrete concepts are perceptionrevealing, they would benefit from a strong emphasis on the perception embedding.

emotion-revealing word groups such as abstract concepts would be opposite.


In the SimLex-999 dataset which focuses on the word similarity, the cognition (lexical relation) and the sentiment modules turned out to be important.

in the WordSim-353 dataset which focuses on the word relatedness, both linear context and syntactic context are turned out to be critical.


the assumption that each word is represented by only one single vector is problematic when dealing with the polysemous words.


Pervious work mostly focus on using clustering to induce word senses (each cluster refers to one of the senses) and then learn the word sense representations respectively (Reisinger and Mooney, 2010;Huang et al., 2012;Tian et al., 2014;Neelakantan et al., 2014;Li and Jurafsky, 2015).

the above approaches ignore the relatedness among the word senses.


Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (Kim, 2014; Kalchbrenner et al., 2014; Johnson and Zhang, 2014; Zhang et al., 2016).

these models require practitioners to specify an exact model architecture and set accompanying hyperparameters, including the filter region size, regularization parameters, and so on.


Emerging research has begun to explore hyperparameter optimization methods, including random search (Bengio, 2012), and Bayesian optimization (Yogatama and Smith, 2015;Bergstra et al., 2013).

these sophisticated search methods still require knowing which hyperparameters are worth exploring to begin with (and reasonable ranges for each).


The best performing strategy is to simply use many feature maps (here, 400) all with region size equal to 7, i.e., the single best region size.

we note that in some cases (e.g., for the TREC dataset), using multiple different, but nearoptimal, region sizes performs best.


The 'best' number of feature maps for each filter region size depends on the dataset.

it would seem that increasing the number of maps beyond 600 yields at best very marginal returns, and often hurts performance (likely due to overfitting).


In the baseline configuration, we performed 1max pooling globally over feature maps, inducing a feature vector of length 1 for each filter.

pooling may also be performed over small equal sized local regions rather than over the entire feature map (Boureau et al., 2011).


4) beddings helps to prevent overfitting (compared to bag of words based encodings).

we are not advocating completely foregoing regularization.


More precisely, i corresponds in this case to an entry and j and k to two of its neighbors such that rank(j) > rank(k).


The result of the process described in the previous section is what we could call a knowledge-boosted distributional thesaurus.

its form is not different from a classical distributional thesaurus and it can be embedded similarly by applying the method of Section 2.


For instance, word senses that are underrepresented or absent in the training corpus will not be assigned a functional embedding.

due to the ability of these models to process large amounts of data, well-represented word senses will acquire meaningful representations.


A characteristic of this approach is that these models can generate embeddings for a complete inventory of word senses.

the dependence on manually crafted resources can potentially lead to incompleteness, in case of unlisted word senses, or to inflexibility in the face of changes in meaning, failing to account for new meanings of a word.


It can be noted that Moses is much more conservative than Nematus and simply tends to copy the original as the output ("Identical" cases).

as the majority (57%) of aligned sentences in the professional Newsela simplifications are edited, we do not consider copying a valid "simplification" in most cases.


We have a small set of discourse connective templates for each one of the 4 class-level PDTB relations (for example, "m i .

m j " is one of the templates for the comparison relation), and we know the relation between the message and the previous message.


In extractive approaches, which are usually applicable in text and ontology summarization (Jones, 2007) (Zhang et al., 2007), a set of features is extracted directly from the input data.

in non-extractive methods, which generally are employed in graph (Navlakha et al., 2008) and database (Bu et al., 2005) summarization, new sentences from the input data are generated (Hahn and Mani, 2000) to form a summary.


It is considered preparing tailored data for motion recognition for each kind of procedure execution videos have high cost because it is often vague even for human annotators to assign every concrete motions into text-level motion categories.

objects directly appear in texts and there is much less ambiguity than motions.


The model from Section 4.3 gives equal importance to the similarity scores from all Wikipedia articles.

it's more intuitive for more relevant articles to have more importance.


It is not appropriate to treat identifying the triggers that contains multiple words as a word classification task, because most of the triggers of multiple words contain prepositions.

the prepositions in such triggers do not trigger event independently.


When generating the unaligned target words , our model only depends on the words previously generated without considering the context of source side.

we redesign the objective function so as to emphasize the partially aligned parts in addition to maximizing the log-likelihood of the target sentence.


It indicates that conventional NMT system cannot learn a good model on the small-scale datasets.

when fine-tuning our partially aligned model with this small parallel corpus, we can get a surprising improvement.


When using more than 60K sentence pairs, we still get a relatively high promotion of translation quality.

the promotion is not very remarkable as Row1-3 reveal in Table 4.


Above methods are designed for different scenarios, and their work can achieve great results on these scenarios.

when in the scenario we propose in this work, that is we only have monolingual sentences and some phrase pairs, their methods are hard to be utilized to train an NMT model.


This architecture has been applied in many applications such as machine translation (Sutskever et al., 2014;Cho et al., 2014b), image captioning (Karpathy and Fei-Fei, 2015), and so on.

such architecture encounters difficulties, especially for coping with long sequences.


Specifically, when the source and the target languages have different sentence structures and the last part of the target sequence may depend on the first part of the source sequence.

although the global attention mechanism has often improved performance in some tasks, it is very computationally expensive.


(Chorowski et al., 2014) also proposed a soft constraint to encourage monotonicity by invoking a penalty based on the current alignment and previous alignments.

the methods still did not guarantee a monotonicity movement of the attention.


Partly inspired by the P&P framework, we use a sequence of binary variables as the latent representation of a language.

there are non-negligible differences between P&P and ours, which are discussed in Section S.2 of the supplementary material.


If a given value, z * ,k , has a relatively large V (z * ,k ), then setting a large value for v k enables it to appropriate fractions of the mass from its weaker rivals.

if too large a value is set for v k , then it will be overwhelmed by its stronger rivals.


It will increase with a rise in the number of phylogenetic neighbors that assume value b.

this probability depends not only on the phy- logenetic neighbors of language l, but it also depends on its spatial neighbors and on universality.


Due to the high ratio of missing values, the model might have overfitted the data with larger K. The fact that SYN outperformed Surface-DIA suggests that inter-feature dependencies have more predictive power than inter-language dependencies in the dataset.

they are complimentary in nature as SYNDIA outperformed SYN.


In the interests of feasible multi-reference evaluation, we pose question and response generation as two separate tasks.

all the models presented in this paper can be fed with their own generated question to generate a response.


Additionally, the dataset did not assign an entity iden- tifier to a pronoun.

as our dataset has access to the manual annotations of coreferences, we are able to investigate the ability of the language model to capture meanings from contexts.


Dynamic updating could be applied to words in all lexical categories, including verbs, adjectives, and nouns without requiring additional extensions.

verbs and adjectives were excluded from targets of dynamic updates in the experiments, for two reasons.


Only inter-sentential implicit relations are annotated in the PDTB, due to time and resource constraints (Prasad et al., 2008).

this does not mean that implicit relations only hold between consecutive sentences.


Both approaches allow the sentiment portion of training and testing data to be in the same vector space.

many languages have no MT system, and it is extremely expensive to create one on a language-by-language basis.


Though POS-tag information can generate dependency relations, we use the PTB3 data to pre-train the bottom level models, where noise may weaken the advantages.

the dependency model contains more detailed information, and is useful for PTB-like formal data.


In the ontology graph construction process, we keep adding unexplored vertices to the vertex set, Domain  Features  Nodes  Edges   Automobile  132  114  778  Camera  986  979  1280  Kitchen  767  670  10629  Software  150  135  842   Table 3: Ontology-graph Statistics as long as, there is at least one edge between the corresponding concept to an existing vertex in the vertex-set, of one of the types functional, hierarchical or synonymous.

we restrict to adding vertices such that the maximum distance between the seed word and the newly added concept remains less than a given threshold n. We empirically fix n = 4, which practically provides a sufficiently large number of concepts that are realistically related to the concept of the seed word.


We further compare our work against the reported approach for the same task by (Mukherjee and Joshi, 2013) which also uses ConceptNet, and has an approach similar to ours.

as mentioned earlier, they consider ontology as a tree while we construct a graph.


Inspired by the models above, the goal of this research is to build a model for exploiting syntax, semantic, sentiment and context of tweets by constructing four kinds of embeddings: Char-AVs, LexW2Vs, ContinuousW2Vs and Depen-dencyW2Vs.

we modify Bi-GRNN of (Chung et al., 2014) into Bi-CGRNN to take word embeddings in order to produce a sentence-wide representation from sentence compositions.


it has been widely used in different evaluation tasks (Go et al., 2009) (Dos Santos and Gatti, 2014).


Therefore, the emoticons would be useful when classifying test data by using deep learning model.

our preprocessing steps are different from (Go et al., 2009), they remove the emoticons out from their training datasets because they revealed that the training process makes the usage of emoticons as noisy labels and if they consider the emoticons, there is a negative impact on classification accuracy.


We use GRUs for our model because GRUs are quite new and their tradeoffs have not been fully explored yet.

gRUs have fewer parameters (U and W are smaller) and thus may train a bit faster or need less data to generalize.


These experiments show that CharAVs and LexW2Vs achieve good performances and contribute in enhancing information for words.

the experiments indicate that  (Go et al., 2009) 83.0 -NB (Go et al., 2009) 82.7 -SVM (Go et al., 2009) 82.


Lastly, while the advertisers can provide prior knowledge for the class they want to target, they cannot accurately specify the irrelevant (or negative) category because it likely covers broad topics in the wild.

to train a classifier, usually labeled instances for each class are required.


Long short-term memories ("LSTMs": Hochreiter and Schmidhuber (1997)), a particular variant of RNN, have become particularly popular, and been successfully applied to a large number of tasks: speech recognition (Graves et al., 2013), sequence tagging (Huang et al., 2015), document categorisation (Yang et al., 2016), and machine translation .


It seems to work very well with English, where it improves performance substantially even though this improvement is not specially significant.

the LM does not improve the performance at all in Spanish.


the based log-likelihood model might favors precision or recall.

from the analysis of the biases, we found no obvious trends favoring precision or recall.


rNNs struggle to train on long term dependencies sufficiently; and therefore, Long Short Term Memory Models (LSTM) and Gated recurrent Units (GrUs)  are more common for such sequence to sequence learning.


They outperform statistical, heuristic, neural single instance and mixture of experts ensemble models over multiple datasets.

these ensemble models are unable to capture the stringent rules and restrictions that disallow certain character combinations like bxy, ii, gls.


This task typically makes use of stylometric cues at the surface lexical and syntactic level (Stamatatos et al., 2015), although Feng and Hirst (2014) and Feng (2015) go beyond the sentence level, showing that discourse information can help.

they achieve limited performance gains and lack an in-depth analysis of discourse featurization techniques.


Ji and Smith (2017) propose an advanced Recursive Neural Network (RecNN) architecture to work with RST in the more general area of text categorization and present impressive results.

we suspect that the massive number of parameters of RecNNs would likely cause overfitting when working with smaller datasets, as is often the case in AA tasks.


The discourse embedding features, on the other hand, manage to increase the F1 score by a noticeable amount, with the maximal improvement seen in the CNN2-DE (global) model with RST features (by 2.6 points).

the discourse-enhanced SVM2-PVs increase F1 by about 1 point, with overall much lower scores in comparison to the CNNs.


We speculate that although the document must be a certain length for discourse to "kick in", these features are effective even with few training examples.

inspecting the gradients of the character bigrams for these cases reveals a higher incidence of 0s, suggesting the bigram feature is not as robust in the smaller sample space.


In our case, this sharing is between two models: On one hand, a standard Sequence-to-Sequence conversational models is trained to predict the current response given the previous context.

using the non-conversational data, we introduce an autoencoder multi-task learning strategy that predicts the response given the same sequence, but with the target parameters tied with the general conversational model.


Our multi-task approaches consistently outperform baseline on perplexity.

the performance between individual target users can vary substantially.


Previous work has explored modeling semantic relationships between messages using a predefined list of technical words (Elsner and Charniak, 2010) and applying Latent Dirichlet Allocation (Adams and Martel, 2010).

we apply the research done by Lowe et al.


The relatively high number of children suggests that typically a message receives more than one reply.

the lower number of direct parents suggests that a message is typically replying to a single parent message.


(2009) who propose using NPIs for shifter extraction.

10 our work substantially extends that previous work.


Another line of work is based on a purely unsupervised learning method, denoising autoencoders, where the hidden layers in multi-layer neural networks are believed to be robust against domain shift (Glorot et al., 2011;Chen et al., 2012;Zhou et al., 2016).

all these methods are still based on traditional discrete representations, and the shared representations are learned separately from the final classifier and therefore not directly related to sentiment classification.


As shown in Table 2, HNN only correctly predicts the sentiment of the first document but gives wrong predictions on another two documents, since worth watching only occurs once in the source Book domain.

our model DSR can make correct predictions for all of them.


and (Kim et al., 2014) proposed cross-lingual annotation projection approach for relation detection with parallel corpora.

our work don't require parallel corpora nor Machine Translation.


English, Chinese) corpora of relation annotation are readily available.

for most languages few (if any) such resources exist.


The first piece of work comparing methods for identifying translation pairs in comparable corpora was presented in Jakubina and Langlais (2016).

the evaluation was conducted on Wikipedia, which is a general domain corpus.


From this point CBOW is more appropriate for our task.

combining both CBOW and Skip-gram as shown by the Concat model, always improves the MAP scores.


This drop may suggest that Skip-gram concatenation is not appropriate for this configuration.

the concatenation of CBOW and Skip-gram models (the Concat approach) still improves the results as we can see for BC (BC ∪ JRC) where we move from 53% to 56.1% of MAP score and for BC (BC ∪ CC) where we increase the MAP score from 68.4% to 70.9%.


In this perspective, all the utterances in the same session is homogenous and could be composed within RNNs.

the influences of user's queries and the agent's responses are different in predicting user's emotion.


Recent works encode the sequence of utterances with recurrent neural network based encoder-decoder to generate responses (Serban et al., 2016;Shang et al., 2015).

in this work, the prediction is made with all existing utterances being known.


There are 2 million sessions in the dataset, most of which contain task-oriented dialogues.

we focus on those only including chat, and the amount of such pure chat sessions is 260,867.


The pre-training data encoding such strong correlation will makes the models ignore the context utterance and convergence to the local optimum only related to R 0 .

the balanced pre-training dataset is effective in initializing the networks.


This way of specifying a dialog model using intents and corresponding system responses manually is more popular in industry than a data driven approach as it makes dialog model easy to interpret and debug as well as provides a better control to a dialog designer.

this is very time consuming and laborious and thus involves huge costs.


Similarly the agents utterances can be clustered to identify system responses.

we argue that rather than treating user utterances and agents responses in an isolated manner, there is merit in jointly clustering them.


and "What OS is installed in your machine" have no syntactic similarity and therefore may not be grouped together.

the fact that these utterances are adjacent to the similar user utterances "I am unable to start notes email client" and "Unable to start my email client" provides some evidence that the agent utterances might be similar.


Also the F1-scores on Seq2Seq based representation improve from 0.83 and 0.9 to 0.86 and 0.916 using SimCluster.

the gains are much more in case of Doc2Vec representations than Seq2Seq representations since Doc2Vec did not have any information from the other domain where as some amount of this information is already captured by Seq2Seq representation.


(2017) introduced a network-based end-to-end trainable task-oriented dialogue system, which treated dialogue system learning as the problem of learning a mapping from dialogue histories to system responses, and applied an encoder-decoder model to train the whole system.

the system is trained in a supervised fashion: not only does it require a lot of training data, but it may also fail to find a good policy robustly due to lack of exploration of dialogue control in the training data.


In the conversation, the agent asks the user a series of Yes/No questions to find the correct answer.

this simplified task may not generalize to practical problems due to the following: 1.


We know, for example, that personality scores are, by construction, Gaussian at the individual level, and that averaging these Gaussians should give a distribution of mean county-level personalities that are Gaussian.

as shown below, we find our county-level predictions to be far from a normal distribution, with some predictions lying over 10 standard deviations from the mean.


2012is related to our research because they also used a bootstrapping method to discover offensive language from a large-scale Twitter corpus.

their bootstrapping model is driven by mining hateful Twitter users, instead of content analysis of tweets as in our approach.


While this classifier aggressively labeled a large number of tweets as hateful, only 121,512 tweets are estimated to be truly hateful.

the supervised LSTM classifier has a high precision of around 79%, however, this classifier is too conservative and only labeled a small set of tweets as hateful.


In this example, the gist of the question is whether the chlorine will stripe the questioner's hair.

the question also contains the additional information that the questioner swims five days a week and has black 1 https://answers.yahoo.com/ Table 1: Example of question-summary pair Question Text: I'm a swimmer for my school swim team and I practice two hours a day, five days a week.


Abstractive approaches, rather than selecting units, generate a summary using words not found in the input text.

existing summarization approaches, whether extractive or abstractive, do not assume a question as an input.


(2005) worked on classification of multiple-sentence questions into classes such as yes/no questions and definition questions, and attempted to extract the question sentence that was the most important in finding the correct class.

the extracted question sentence is not always a summary of the question.


In Example 1 of Table 4, the questioner accidentally spilled buttered popcorn and needs to know how to remove it.


In this example, a summary can be generated by extracting the last sentence in the original content.

if one takes the actual title into consideration, the idiom "get rid of" in the original question can be replaced by the word "remove".


The original data contains 4,485,032 question-title pairs.

not all of them are question-summary pairs.


There was no length constraint in terms of the number of characters or words.

we assumed that a better summary would contains more focused content in a shorter output.


These examples suggest that extractive approaches are intrinsically not suitable for cases where informa-tion needs to be picked up from multiple sentences in the input.

to extractive approaches, the output of CopyNet properly resolves this; "it" is resolved by "The Simpsons" even if the model needed to use information across sentences.


The selection of a summary-worthy subset of all extracted concepts and relations was largely ignored in previous work, as many studies did not have a focus on summarization.

when dealing with larger document clusters, this step becomes inevitable.


After predicting scores for every concept and selecting the highest scoring subgraph with the ILP, we use this subgraph as the summary concept map.

this graph might contain multiple edges between certain concepts.


Both of the above approaches rely on sequential arrangement of phrasal or subsentential structures existing in the input corpus to generate summary.

an ideal abstractive multi-document summarizer should enjoy the freedom to exhibit its own writing style and to generate the sentences from scratch.


The three layers correspond to the most valuable information sources for the baseline.

the baseline draws only simple categorical features from them.


The middle-left part of Figure 3 depicts the biL-STM and its final output, the average vectors a.

to LSTMs, which are designed to capture the meaning of sequences, Convolutional Neural Networks (CNNs) are often used to pro- duce bag-of-words-like representations.


We pick the class with the highest probability as our final result.

choosing between all classes is unnecessary because not all combinations of event type, entity type and argument type are possible.


Input to their network is the word embedding matrix of a sentence.

to , they predict triggers and arguments jointly.


As shown in Table 2 under the SRL column, these features only give a minor improvement in micro-averaged F1 for the EED sieve, but hurt performance of the other sieves and the architecture on the TimeBank-Dense test set.

we believe this may be due to overfitting, as we observe 3% and 4% gains for the ETWS and EED sieves with these features on the development set.


CAEVO uses Algorithm 1 to draw inferences about a data set D. In the original implementation, this set contained only the gold-standard labeled evaluation pairs within two sentence windows.

if D were expanded with other unlabeled data points outside of two sentence windows (for which it is easy to predict labels with high precision), the transitivity constraints in C might generate further high precision predictions on the labeled data.


Previous open Relation Extraction (open RE) approaches mainly rely on linguistic patterns and constraints to extract important relational triples from large-scale corpora.

they lack of abilities to cover diverse relation expressions or measure the relative importance of candidate triples within a sentence.


Most successful open RE approaches (Fader et al., 2011;Xu et al., 2013;Bovi et al., 2015;Bhutani et al., 2016) extract salient relational triples based on lexical or syntactic patterns.

such handcrafted or automatically learned patterns are incapable of covering diverse relation expressions (Soderland et al., 2013).


The way we rank nodes is most similar to the work of White and Smyth (2003) and Yu and Ji (2016) which generate the relative importance score of each node toward a set of preferred nodes.

they only deal with unweighted undirected graphs.


Although the system explicitly combines biological events extracted from sentences to construct a long biological process, possibly leading to the propagation of errors, they reported fairly good performance and meaningful results, considering that it is the first attempt for document-level biological information extraction.

there are quite a few systems and corpora for document-level comprehension from a similar perspective in other domains, such as news articles (Hermann et al., 2015) and children's books (Hill et al., 2016).


In the attentive reader and the static RNN decoder, we used LSTM (Hochreiter and Schmidhuber, 1997), a variant of the RNN model, and set the hidden size and dropout rate of RNN to 64 and 0.5, respectively.

the sequenceto-sequence model used GRU (Cho et al., 2014), another variant of the RNN model, and we set the hidden size of 64 and dropout rate of 0.5.


If the reader model and the sequence-tosequence model use attention as demonstrated in other studies, it shows higher performance than the model without attention.

the static RNN decoders show a different case, in that our proposed attention seems to hamper the model in exactly extracting the environment.


Overall, precision of the static RNN decoder is found better than that of the other models.

the attentive reader model shows outstanding performance in extracting all relevant environment tokens.


Although there are many answer tokens, the static RNN decoder seems to extract minimal tokens.

the majority of tokens extracted by the Figure 3: Performance of the two models, the attentive reader and the static RNN decoder model, according to the number of environment tokens.


Experimental results show that our method significantly outperforms all the alternative methods (+0.013-0.318 in terms of Rprecision; p 0.01, t-test).

with traditional courses that have limited numbers of students, each online course in a MOOC platform may draw more than 100,000 registrants (Seaton et al., 2014).


Because a course concept is likely to connect with other course concepts with high semantic relatedness, it tends to receive high voting scores from its course concept neighbors, compared with other vertexes, during propagation.

in practice, the propagation process is usually hampered by the overlapping problem.


TPR alleviates this problem by incorporating topic information from online encyclopedias.

tPR also favors frequent course concepts, because frequent words tend to have high connectivity in the cooccurrence graph, and thus receive high rankings using PageRank.


In the age deceiver case, the younger individuals who portray themselves as older use   more 'you', 'family', 'death' and 'filler' words.

older individuals who portray themselves as younger use more internet slang 'netspeak', 'fillers', and 'swear' words.


The Real Male vs Female as Male correlation suggests that females are good at emulating males' writing.

the analysis shows that males are not as good emulating female language (see correlation of Real Female vs Male).


The main rationale for these two fields is for users to provide an accurate textual description for each query they make.

there is no method to ensure that the title or the description entered for a query are actually relevant in describing it.


First, most of the SQL snippets are relatively simple, containing at most 10 distinct tokens, as can be easily seen in Table 2.

textual descriptions are more evenly distributed, based on the number of tokens, with 2, 003 of the entries in the dataset having more than 100 tokens.


On the other hand, the generated SQL queries are more often than not inaccurate and thus we have not compared the accuracy of the NNLIDB with existing solutions.

we have focused on verifying how similar the generated SQL queries are to the annotated ones using measures from machine translation (BLEU) and also precision and recall for simpler tasks, such as generating the correct table and column names in a SQL statement.


Approaches to the automatic generation of system actions, such as (Kadlec et al., 2015), have been presented to facilitate that process.

those approaches often consider only system actions that are necessary from a functional point of view.


Overall, CG statements perform well compared to HG ones.

there are cases when the rating of CG generated statements decreases for one or both of the considered cultures when compared to the HG statement.


In addition to the message conveyed in a given tweet, some information is stored for all tweets, such as location, time and user's profile.

this information is not accessible for most tweets.


In this particular case, each word will be represented by a vector of 4k continuous values.

we need to represent tweets with variable lengths based only upon the concatenated embeddings of the words they contain.


(Aw et al., 2006;Pennell and Liu, 2011).

social network service (SNS) text has a dynamic nature, and large SNS text is costly to annotate.


The basic framework of Japanese normalization is quite similar to that of English normalization.

the problem is more complicated in Japanese normalization because Japanese words are not segmented using explicit delimiters, so we have to estimate word segmentation simultaneously in the decoding step.


(2013) proposed a character-level sequential labeling method for normalization.

it handles only oneto-one character transformations and does not take the word-level context into account.


In these studies, clear word segmentations were assumed to exist.

since Japanese is unsegmented, the normalization problem needs to be treated as a joint normalization, word segmentation, and POS tagging problem.


In a previous study (Han and Baldwin, 2011), the nodes were assumed to be single words.

our  Figure 3: Flow of generating coarsely segmented corpus method assigns a node to not only single words but also short phrases (See.


Real-world applications include GitHub, version control in Microsoft Word and Wikipedia version trees (Sabel, 2007).

our system solves an n-to-n problem on a large scale.


Fixed-length fingerprints are created using hash functions to represent document features and are then used to measure document similarities.

the main purpose of fingerprinting is to reduce computation instead of improving accuracy, and it cannot capture word semantics.


According to the description of DTW in Section 3.1.3, the distance between two documents can be calculated using DTW by replacing each element in the feature vectors A and B with a word vector.

computing the DTW distance between two documents at the word level is basically as expensive as calculating the Levenshtein distance.


wDTW and wTED perform better than WMD especially when the corpus is large, because they use dynamic programming to find the global optimal alignment for documents.

wMD relies on a greedy algorithm that sums up the minimal cost for every word.


Fact-checking can be framed as a questionanswering (QA) task in a broad sense.

it has not been studied as intensively as other QA tasks such as factoid-style question-answering (Ravichandran and Hovy, 2002;Bian et al., 2008;Ferrucci, 2012).


The document score ds is defined by Term Frequency-Inverse Document Frequency (TF-IDF), which is written as where tf (w ′ , d) is the frequency of the word w ′ in the document d, idf (w ′ ) is the inverse document frequency of the word w ′ , and ℓ(d) is the length of d. The VQA feature uses document-local statistics (except for IDF) and counts only in the topk search results.

in this feature, all the query words jointly contribute through the document score ds, in contrast to the case of PMI where only the pairwise relations are considered.


The difference between them is that they use the longest consecutive common subsequence between question and KB entity names to get candidates in the passive linker.

the active linker first gets the span from the question that is most likely to be a subject entity by sequential labeling, and the linker uses the labeled span to search the candidate entity from KB.


is "Excuse me, but can you tell me the way... Just go straight... You can t miss it", whose dialog act flow {3, 3} is consistent with the context test.

the response found by feature-based approach has the context "Can you direct me to Test Context Retrieved Response U1: Can you direct me to Holiday inn ?


These findings are consistent with previous work (Sordoni et al., 2015;Serban et al., 2016b).

the first three columns in Table 4 show that models pretrained by OpenSubtitle converge faster, achieving lower Perplexity (PPL) but poorer BLEU scores.


To address this issue, we take inspiration from the FraCaS dataset (Cooper et al., 1996) and construct a suite of targeted datasets that separately test a system's ability to perform individual bits of interpretation such as paraphrasing, semantic role labeling, and coreference.

to the original FraCaS data set, which is relatively small and which could not support the training of purely lexical neural RTE classifiers, we pursue the strategy of automatically converting semantic classificationsi.e., human judgments about semantic propertiesinto labeled examples for textual entailment.


Traditionally, they rely on domain-specific lexicons (de Does and Depuydt, 2013) and characterbased errors statistics obtained from a corrected training set (Kumar and Lehal, 2016).

they have some drawbacks that limit their usefulness for specific, low-resource domains.


Previous studies in document classification attempted to address these issues by employing multilingual word embeddings, which allow direct comparisons and groupings across languages (Klementiev et al., 2012;Hermann and Blunsom, 2014;Ferreira et al., 2016).

they are only applicable when common label sets are available across languages which is often not the case (e.g.


The latter is defined as a concatenation of the hidden states for each input vector obtained from the forward GRU, g w , and the backward GRU, g w : The same concatenation is applied for the hiddenstate representation of a sentence h A typical way to obtain a representation for a given word sequence at each level is by taking the last hidden-state vector that is output by the encoder.

it is hard to encode all the relevant input information needed in a fixed-length vector.


For minibatch SGD, the number of samples per language is equal to the batch size divided by M.   Multilingual document classification datasets are usually limited in size, have target categories aligned across languages, and assign documents to only one category.

classification is often necessary in cases where the categories are not strictly aligned, and multiple categories may apply to each document.


In their method, based on the alignment between a nonterminal and input words, the attention mechanism has also an important role.

since the attention is learned in an unsupervised manner, the alignment quality might not be optimal.


Since the process of creating annotated resources needs significant manual effort, SRL resources are available for a relative small number of languages such as English (Palmer et al., 2005), German (Erk et al., 2003), Arabic (Zaghouani et al., 2010) and Hindi (Vaidya et al., 2011).


We would like to score every path in the lattice with the NMT system and then search.

this is generally prohibitively expensive because the RNN architectures in NMT do not permit recombination of hypotheses on the lattice, since NMT states encode the entire sentence history.


Word embeddings are a relatively new addition to the modern NLP researcher's toolkit.

unlike other tools, word embeddings are used in a black box manner.


If a word embedding learning algorithm wishes to model this information correctly, it has to strive to uphold this equality constraint.

its success will depend on the degree of freedom which it receives in terms of the number of dimensions.


We also use SEQ2SEQ for prediction of what comes next in a text.


Traditional language models are based on contextual word frequency in a static corpus of text.

certain types of phrases, when offered to writers as suggestions, may be systematically chosen more often than their frequency would predict.


Language models have a long history and play an important role in many NLP applications (Sordoni et al., 2015;Rambow et al., 2001;Mani, 2001;Johnson et al., 2016).

these models do not model human preferences from interactions.


6 This model can be used for generation: sampling from the model yields words or phrases that match the frequency statistics of the corpus.

rather than offering representative samples from h 0 , most deployed systems instead sample from p(w i ) / h 0 (w i ) 1/⌧ , where ⌧ is a "temperature" parameter; ⌧ = 1 corresponds to sampling based on p 0 (soft-max), while ⌧ !


The complexity for the gradient calculations is also O(n 3 ).

even though our vectorised version has higher complexity, in practice we see large gains in runtime.


Other studies use transliterations to segment katakana words using explicit word boundaries from the original English words (Kaji and Kitsuregawa, 2011;Hagiwara and Sekine, 2013).

as not all katakana words are transliterations, it is advantageous to use a monolingual corpus.


This is adequate for two-character words in Chinese, which comprise the majority of Chinese words (Suen, 1986), but not for potentially very long katakana words in Japanese.

to their approach, we regard each katakana term as one document and compute the inverse document frequency.


Recent researches (Sun, 2011;Qian and Liu, 2012;Zheng et al., 2013;Zeng et al., 2013;Qian et al., 2015) also focus on the development of a joint model to perform Chinese word segmentation, POS tagging, and/or informal word detection.

to the best of our knowledge, no existing system can perform word segmentation, POS tagging, and NER simultaneously.


It is also reported by  that segmentation criteria in AS and CU datasets are not very consistent.

by fusing two corpora, the RNN CU06+YA can even surpass the performances of CKIP.


Their corresponding recalls are therefore equal as TP is normalised by RP, which is hard-constrained by the references.

adopting precision as the metric, S1 yields substantially higher scores as it returns much fewer PP.


For WS, it is not straightforward to compute TNR by directly normalising the true negatives (TN) by the real negatives (RN).

it can be indirectly computed via TP, PP, RP and the total number of possible output TW given a sentence.


AAS and MAS deal with many-to-many and oneto-many word alignments, respectively.

hAS (Song and Roth, 2015) is based on one-to-one word alignments.


These systems use general language models not restricted to individual application domains.

for an ASR in a larger pipeline, the expected words and phrases from users will be biased by the application domain.


Case markers of the other dependents Our model independently estimates label scores for each argument candidate.


(2015) using a feedforward network for calculating the score of the PAS graph.

the model is evaluated on a dataset annotated with a different semantics; therefore, it is difficult to directly compare the results with ours.


Empirical studies such as (Kita andÖzyürek, 2003;Kita et al., 2007) analysed speech and gesture semantics with statistical methods and show that the semantics of speech and gestures coordinate with each other.

it remains unclear how to computationally derive the semantics of iconic gestures and build corresponding multimodal semantics together with the accompanying verbal content.


This problem has theoretic roots in psycholinguistics studies such as  and , which aim to understand connections between emotions and words.

emotion Analysis also has motivations from an applied perspective, being closely related to Opinion Mining (Pang and Lee, 2008).


a simple way to have a flexible non-linear model over the data.

from a GP perspective it assumes the process is infinitely mean-square differentiable.


Compared to our model, they show much lower correlation scores (their best model achieves 0.399 Pearson's r on the SemEval2007 dataset), although these are not strictly comparable since they use different data splits and do not perform cross-validation.

their approach is orthogonal to ours: combining the Matérn kernels within a multi-task GP framework can be a promising avenue for future work.


MTNA-s. To evaluate to what extent that ATE loss function can improve the performance of the ACC task, we compare MTNA with its variance MTNA-s, the loss function of which does not include that of ATE task.

this model keeps LSTM layer as a feature extractor before the convolution layers as MTNA does.


The reason is that some sentences have restaurant names as target terms.

there are around 40.1% sentences with Restaurant label that do not have annotated words in the training dataset, 41.2% in test dataset.


(Collobert et al., 2011) proposed a multi-task learning system using deep learning methods for various natural language processing tasks.

the system with window approach cannot be jointly trained with that using sentence window approach.


With the power of deep learning, there are emerging better approaches of LU (Hakkani-Tür et al., 2016;Chen et al., 2016b,a;.

most of above work focused on single-turn interactions, where each utterance is treated independently.


Table 1 shows the similar trend as LU results, where applying either rolebased contextual models or intermediate guidance brings advantages for both semantics-encoded and NL-encoded history.

to NL, semantic labels (intent-attribute pairs) can be seen as more explicit and concise information for modeling the history, which indeed gains more in our experiments for both LU and dialogue policy learning.


Neural conversation systems, typically using sequence-to-sequence (seq2seq) models, are showing promising progress recently.

traditional seq2seq suffer from a severe weakness: during beam search decoding, they tend to rank universal replies at the top of the candidate list, resulting in the lack of diversity among candidate replies.


This is why summary writing is sometimes used together with essays to assess university-level abilities in L2.

assessing L2 summaries is highly demanding, especially if analytic rubrics are involved, as they require raters' expertise and much concentration when assessing language proficiency at various levels (e.g., lexis, syntax, discourse).


Analytic scoring based on different rubrics (e.g., accuracy, cohesion) is therefore particularly appropriate when assessing summaries in L2 as it offers more informative feedback (Bernhardt, 2010) and better captures different facets of L2 writing competence than holistic assessment (Weigle, 2002).

analytic scoring is often exceptionally demanding for raters, especially in the case of longer texts and more than four or five scoring categories (CEFR), which motivates the use of automated assessment.


Our method outperforms the previous studies (TextRank (Mihalcea and Tarau, 2005), L2R (Henß et al, 2015), Regression (Henß et al, 2015), RL-full (Henß et al, 2015) ) on ACL-ARC and WIKI data but achieves lower performance than the previous studies on DUCs.

the differences of ROUGE scores between the models are relatively small.


The PEAK (Yang et al., 2016) method for evaluation automates the extraction part of the SCUs, and they use the ADW (Align, Disambiguate and Walk) algorithm (Pilehvar et al., 2013) to compute semantic similarity.

their approach fails to model contradiction, paraphrase identification and other features like natural language inference.


Since SSAS performs similarity assessment using deep semantic analysis, it does take significantly large amount of execution time compared to other methods.

ssAs computations for multiple summaries can be easily parallelized.


They have also been developed for the update summarization task, with work such as (Li et al., 2015) about the weighting of concepts.

the most obvious weakness of such methods, particularly the one proposed by Gillick and Favre (2009), is their implicit way of modeling information redundancy.


The improvement over ICSISumm 2009, which has the same settings as our system, confirms the interest of handling redundancy explicitly in the update summarization task.

the improvement over ICSISumm-BG-DOWN-1&2 also shows that basic methods for performing this handling are not efficient in ILP models, contrary to our sentence semantic clustering approach.


(2016) used the word embeddings in order to perform query expansion.

previous word embedding-based probabilistic IR methods have no theoretical validity since the word embeddings were heuristically introduced.


Clearly, the local window of contexts is filled with irrelevant words, meanwhile, it fails to capture important temporal status indicators.

as shown in figure 1, the event "launch" is actually a high order indirect governor word of the target event word "protest" and is only two words away from the target event in the dependency tree.


Other approaches include shortest path kernels , graph kernel (Airola et al., 2008), composite kernel (Miwa et al., 2009), subsequence kernels (Kim et al., 2010), and tree kernels (Eom et al., 2006;Qian and Zhou, 2012).

engineering features from different sources may not lead to optimal results.


When using randomly initialized embeddings, RNN exhibits similar performance as other NN models.

by taking advantage of pre-trained embeddings, RNN further advances Fscores by 7% and 13% on AIMed and BioInfer, respectively.


used the result of analyzing social support in OHCs for identifying influential users.

these works address emotional support in general and do not focus on identifying empathetic messages.


Note that firstly, LSTM without profile does not perform better than CNN-Wang.

other studies show that when attention model is incorporated, LSTM generally outperforms that of CNN model (Chen et al., 2016;Yang et al., 2016) which will be shown later.


We can also simply apply an encoder-decoder model to text normalization tasks.

it is well-known that encoderdecoder models often fail to perform better than conventional methods when the availability of training data is insufficient.


Other text normalization task In this study, we evaluated our methods with Japanese dialect data.

these methods are not limited to Japanese dialects because they do not use dialogspecific information.


General purpose language identification tools such as TextCat (Cavnar and Trenkle, 1994) and langid.py (Lui and Baldwin, 2012) can identify 50-100 languages with accuracy of 86-99%.

these tools have not considered discrimination between closely related language varieties.


In all four languages, the top linked language is same as a target language.

their percentages vary from 78.08-94.26%, indicating differences in the amount of texts in different languages.


With the rapid development of research on Neural Machine Translation (NMT), translation quality has been improved significantly compared with traditional statistical based method (Bahdanau et al., 2014;Cho et al., 2015;Zhou et al., 2016;Sennrich et al., 2015).

training efficiency is one of the main challenges for both academia and industry.


As it can be seen, boost outperforms the default method by +1.49 (BLEU) at the cost of using 10% of additional training data.

the system converges faster than any other system as best performance is achieved at epoch 14, while others need to achieve the best after epoch 16.


We reported that the majority vote did not consistently improve the BLEU score.

this study develops a voice classifier using the following seven features.


SrcVoice represents the voice of the source side.

unlike English, it is difficult to formulate simple rules to obtain the voice of Japanese 3 Experiments Here we explain the voice controlling method proposed by Yamagishi et al.


Subword units like character (Vilar et al., 2007;Tiedemann, 2009), orthographic syllables (Kunchukuttan and Bhattacharyya, 2016b) and byte pair encoded units (Kunchukuttan and Bhattacharyya, 2017) have been used with varying degrees of success.

if no parallel corpus is available between two languages, pivot-based SMT (Gispert and Marino, 2006;Utiyama and Isahara, 2007) provides a systematic way of using an intermediate language, called the pivot language, to build the source-target translation system.


Therefore we only extract top-level functions since they are usually small and relatively self-contained, thus we conjecture that they constitute meaningful units as individual training examples.

in order to support research on project-level code documentation and code generation, we annotate each sample with metadata (repository owner, repository name, file name and line number), enabling users to reconstruct dependency graphs and exploit contextual information.


In contrast, methods using radial-basis function (RBF) kernels do not provide weight vectors, via which we cannot obtain each feature's importance.

we used SVR-RBF, SVR with a radial-basis function (RBF) kernel, GPR-RBF, GPR with an RBF kernel, and JGPR-RBF, GPR with an RBF kernel and joint prediction ( §3) since these models can take into account combinations of features using the RBF kernels, which are useful for combining both domain and semantic features.


As shown in figure 2, in the case of CCA and Word2vec, bio-markers are distributed evenly around COPD.

in the case of GloVe, COPD is found to be far apart.


However, in the case of GloVe, COPD is found to be far apart.

in all three models, bio-markers located close to COPD are CC-16 and CEA.


This shows that the CCA does not play a significant role in this problem.

word2vec is a very stable methodology because all the BEST marker values show higher values than the wORST cases, unless the dimension is reduced by t-SNE.


On the other hand, Word2vec is a very stable methodology because all the BEST marker values show higher values than the WORST cases, unless the dimension is reduced by t-SNE.

gloVe showed a stable appearance as the number of dimensions increased, while it didn't in case of 2and 5-dimension.


Table 2, we observe that the SPLITEM-BEDDING method, which is the embodiment of Scheme 1 (given in Section 2.1) where σ(w i , l j ), the semantic similarity of word w i with the label l j , is computed using embedding, yields the highest precision across all the methods.

sPLITMULTIEMBEDDING, a variant of scheme 2 (given in section 2.2) where the embeddings of section header and body are independently computed, and a weighted combination of the embeddings is used to retrieve the representative words of the section to compare with the embeddings of the labels, delivers the highest recall and Fscore values, as well as, the highest overall accuracy.


This also applies for CVs that have separate section headers for identification, such as Name, Email etc.

for sections that are intuitively more complex, show some (meaningful) confusions across classes.


(2016) investigated zero anaphoric resolution on news text in Japanese and they considered only the zero arguments of a predicate.

in math problems, the arguments of unsaturated nouns are often omitted.


From this result, we can say that using only Per-ceivedInfo as system utterances is not an effective method.

since there may be a difference among the types of PerceivedInfo, we further investigated the evaluation scores in each type of PerceivedInfo.


If there is fully labelled data available, e.g., labelled documents, our model will account for the full supervision from document labels during the generative process, where each document can associate with a single class label or multiple class labels.

if the dataset contains both labelled and unlabelled data, our model will account for the available labels during the generative process as well as incorporate the labelled features as above to constrain the Dirichlet prior.


For instance, the two topics under Performance+ suggests that some people feel the system performs better and app runs faster, whereas Performance-seems to show highly contrastive opinion that people have bad experience after upgrade, e.g., app crashes or freezes, mac becomes slow.

it is still impossible to accurately interpret the extracted topics solely based on its multinomial distribution, especially when one is unfamiliar with the topic domain.


(Mencap, 2002;PlainLanguage, 2011;Freyhoff et al., 1998).

manual production of texts from scratch for each target population separately cannot keep up with the amount of information which should be accessible for everyone.


Around 90% of CPs have been selected by at least two annotators (see Table 1).

when we separate the selections made by native and nonnative speakers, we see that: (1) the percentage of multiple selected CPs by native speakers and non-native speakers decreases; (2) the percentage of multiply selected CPs by non-native speakers is always lower (83%-85%) than the percentage of multiply selected CPs by native speakers (84%-86%), regardless of the text genre; and (3) the percentage of CPs selected by at least one native and one non-native annotator is lower for the NEWS genre (70%) than for the WIKINEWS and WIKE-PEDIA genres (77%).


Inspired by the success of the data-driven approach, one can prepare a corpus of conversations for every possible style and feed it to a seq2seq.

in order to obtain a stylistically consistent DRG system through a vanilla seq2seq, this method requires millions of training instances for each target style, which is prohibitively expensive.


In the past two decades, the majority of NLP research focused on developing tools for the Standard English on newswire data.

the nonstandard part of the language is not well-studied in the community, even though it becomes more and more important in the era of social media.


Recently, summarization has also been considered as a submodular function maximization (Lin andBilmes, 2010, 2011;Dasgupta et al., 2013) where greedy algorithms were adopted to achieve near optimal summaries.

the main drawback of all the extractive approaches is that they can not avoid the inclusion of insignificant information which degrades the summary quality.


However, the main drawback of all the extractive approaches is that they can not avoid the inclusion of insignificant information which degrades the summary quality.

the abstractive approach in a multi-document setting aims at generating summaries by deeply understanding the contents of the document set and rewriting the most relevant information in natural language.


relevant (P ∪ U) and irrelevant (N ), as was done in SemEval.

by ignoring the difference in utility between paraphrases and useful questions during training, a binary classification approach is likely to underperform a ranking approach that is trained on all the ranking triples implied by the original 3 categories of questions.


To also use the subject, one could simply concatenate the subject and the body (Body + Subj) and apply the same SLA architecture from Figure 1.

as shown in Table 6, this actually hurt performance, likely because the system did not know where the question body started in each input sequence.


(1) Initialize matching with singletons relations  (u, v).

relates a child v of e to d by considering the set U that aligns to v. For each u ∈ U , we increment the score if its parent is d and give additional points if the parent's POS tag and edge label is the same as those of e. these are normalized by |U |.


While monolingual insights like paraphrases have potential applications in semantic textual similarity (Agirre et al., 2012), there exist bigger corpora for those tasks, such as PPDB (Ganitkevitch et al., 2013).

as the Bible is often the only significant parallel text for many of the world's languages, improved 27-way consensus English POS tags Before corpus alignments: TIME: NN (1.00) SECRET: NN (0.54), JJ (0.46) With corpus alignments: TIME: NN (0.94), NNS (0.05) .


1 BabelNet has been applied in many natural language processing tasks, such as multilingual lexicon extraction, crosslingual word-sense disambiguation, annotation, and information extraction, all with good performance (Elbedweihy et al., 2013;Jadidinejad, 2013;Navigli et al., 2013;Ehrmann et al., 2014;Moro et al., 2014).

to the best of our knowledge, to date there is no comprehensive work applying BabelNet knowledge to SMT tasks.


The EN-PL BabelNet dictionary contains 6,199,888 bilingual entries.

the raw data contains a lot of noise, so we performed some pre-processing of the dictionary, including: • if East Asian characters are included in either the English or Polish side, we remove this pair; • if the English side contains symbols which are neither letters nor digits, then we remove this pair; • if either side contains punctuation, then we remove this pair; • if the English side is the same as the Polish side, then we remove this pair; • if the ratio of the word-level entry lengths between the English side and the Polish side is less than 0.5 or greater than 2, then we remove it.


We can see that the Ba-belNet API method did not beat the best domain-adaptation method on all tasks in terms of BLEU and TER.

it does improve system performance compared to the baselines, which shows that using BabelNet can alleviate the issue of unknown words to some extent.


They are clearly useful for indicating certain overarching trends, but say little about actual improvements for translation buyers or post-editors.

these metrics are commonly referenced when discussing pricing and models, both with translation buyers and service providers.


Using several of them in parallel helps to validate the individual scores and thus increase confidence in the results.

while they are very useful for indicating quality trends (for example system B with a BLEU of 79 can quite safely be expected to be better than old system A with a BLEU of 60), their interpretation remains cryptic to many users: "What does it mean if my English to Polish MT system gets a BLEU of 50, is this good or bad?


the Edit Distance score by itself does not take into account the actual time spent on a given translation unit and provides an absolute score, whereby the maximum number of edits possible per segment would be the total number of characters of the reference translation.


This percentage is also frequently quoted in the context of the post-editor recruitment process or as explanation to translation buyers, as MT output, reference translation and expected effort are shown side-by-side.

it still does not consider the actual time spent making the edits.


Sentiment analysis systems, including the best performing ones such as the NRC-Canada system Kiritchenko et al., 2014a;Zhu et al., 2014), use sentiment lexicons to obtain significant improvements (Wilson et al., 2013;Pontiki et al., 2014;Rosenthal et al., 2015;Mohammad et al., 2016a).

much of the past work has focused on English texts and English sentiment lexicons.


As shown above, lexicons created by translating existing ones in other languages can be beneficial for automatic sentiment analysis.

the above experiments do not explicitly quantify the extent to which such translated entries are appropriate, and how translation alters the sentiment of the source word.


A similar happening takes place in community 19 between the Primary Sentiment level and topic 3 level although in this case it was found 4 users with exactly the same sentiment in topic 1.

there are communities that are only present in some topics, such as communities 3, 7, 8, 11, 12 or 19.


It has been used in many different research works over the past years.

we have also used the Bing Liu's sentiment lexicon (Hu and Liu, 2004).


General domain datasets, like the whole Wikipedia data or News dataset from online newspapers, capture very well general syntactic and semantic regularities.

to capture in-domain word polarities smaller domain focused dataset might work better (García-Pablos et al., 2015).


The restaurant-adjectives-test-set contains 119 positive adjectives and 81 negatives adjectives, while laptops-adjectives-test-set contains 127 positives and 73 negatives 11 .

we have used the SemEval 2015 task 12 datasets 12 .


Furthermore, as has been mentioned in many studies (Brody, 1985), (Hall et al., 2000) the gender difference in emotional expression has been detected during several psychological investigations.

to the very specific nature of speaker-adaptive ER, gender-adaptive ER might be more general.


The results may be even better if the SI component performs more accurately (compare E and G systems in Figure 1).

the performances of the Sep system dropped on Emo-DB, LEGO, and Ruslana.


Detailed analysis reveals that it is extremely difficult (for both models) to distinguish Slovak adverbs from nouns.

prepositions are moderately difficult with the c7 model (P=48%, R=75%) but they are practically solved with the Czech model (P=R=99%).


The presence of anger was always signaled by the shouting or use of offensive language.

there are other ways of user's to express their (dis)satisfaction towards the system.


the length of the utterances in the MapTask corpus).

when the classifier is trained on the combination of the MapTask corpus with other corpora, the performance of the classifier improves.


Moreover, the clear separation of dialogue dimensions and communicative functions, coupled with the hierarchical organization of the latter, allows for classification at different levels of granularity.

re-annotating existing corpora with the new scheme might require significant effort.


Yet, also for nouns in general, adjectives and verbs the findings indicate that OCE may have a potential to improve the learning of their semantic similarity-or perhaps relatedness-properties.

we also found significant disadvantages of the OCE-model used here, especially on test sets of similarity judgments.


Corpora annotated with coreference are arguably a valuable resource for such studies.


The corpus, sized over 600 sentences, was manually annotated with full-fledged coreference chains in both languages.


In PCEDT, we did our best to keep this convention.

in some cases coreferential relations between nominal groups without definite determiners are so obvious that they were annotated even in Ontonotes, in spite of negative instructions (as in Example (7)).


Thus, in some cases it is difficult to decide whether a given nominal group should be annotated for coreference.

generic nouns may be used anaphorically and with a determiner (as in Example (8)) inciting annotators to mark a coreferential relation, regardless the annotation instructions.


As the occurrence of entities with no counterparts may be, among other reasons, attributed to grammatical differences, such cases can hardly disappear.

around 6% of the entities with the 1:N mapping must be a result of an error, either in alignment or coreference.